Abstract: Systems, computer program products, and methods are described herein for generating optimized queries for disparate data sources. The present invention is configured to identify a query; determine, based on a parsing of the query, at least one attribute and at least one intent of the query; generate a map comprising the query, the intent, and the attribute; apply the map to a cluster of maps, wherein the cluster of maps comprises the intent associated with a secondary attribute different from the attribute, and a secondary query; generate, based on the cluster of maps, a heat map of the cluster of maps, wherein the heat map comprises an indication of a computer processing consumption of a query with the intent and the attribute, and the secondary intent with the secondary attribute and the secondary query; and determine, based on the heat map, an optimized query for the intent.

Abstract: Systems, computer program products, and methods are described herein for generating optimized queries for disparate data sources. The present invention is configured to identify a query; determine, based on a parsing of the query, at least one attribute and at least one intent of the query; generate a map comprising the query, the intent, and the attribute; apply the map to a cluster of maps, wherein the cluster of maps comprises the intent associated with a secondary attribute different from the attribute, and a secondary query; generate, based on the cluster of maps, a heat map of the cluster of maps, wherein the heat map comprises an indication of a computer processing consumption of a query with the intent and the attribute, and the secondary intent with the secondary attribute and the secondary query; and determine, based on the heat map, an optimized query for the intent.

Abstract: Aspects of the disclosure relate to generating and providing enhanced user interfaces by implementing data, AI, intents and personalization (DAIP) technology. In some embodiments, a computing platform may receive, from enterprise computing infrastructure, first unstructured activity data associated with a first enterprise channel. Subsequently, the computing platform may identify one or more user intents by applying an intent discovery model to the first unstructured activity data associated with the first enterprise channel. Then, the computing platform may generate status tracking information based on identifying the one or more user intents.

Abstract: Aspects of the disclosure relate to generating and providing enhanced user interfaces by implementing data, AI, intents and personalization (DAIP) technology. In some embodiments, a computing platform may receive, from enterprise computing infrastructure, first unstructured activity data associated with a first enterprise channel. Subsequently, the computing platform may identify one or more user intents by applying an intent discovery model to the first unstructured activity data associated with the first enterprise channel. Then, the computing platform may generate status tracking information based on identifying the one or more user intents.

Abstract: A memory stores a first log and a collection of queries. A processor accesses a second log that includes a first, second, third and fourth historical query, determines that the first and second historical queries belong to a first similarity group and the third and fourth historical queries belong to a second similarity group, and ranks the queries within groups based on static and dynamic features. The processor further determines the third historical query is frequently submitted and copies the highest ranked similar queries into the collection. The processor receives a first database query, stores it in both logs, determines it is not similar to queries stored in memory, further determines that it belongs to the first similarity group, copies the highest ranked similar queries to the first log, and returns a report containing the similar queries. The processor further receives and executes a second query selected from the report.

Abstract: A memory stores a first log and a collection of queries. A processor accesses a second log that includes a first, second, third and fourth historical query, determines that the first and second historical queries belong to a first similarity group and the third and fourth historical queries belong to a second similarity group, and ranks the queries within groups based on static and dynamic features. The processor further determines the third historical query is frequently submitted and copies the highest ranked similar queries into the collection. The processor receives a first database query, stores it in both logs, determines it is not similar to queries stored in memory, further determines that it belongs to the first similarity group, copies the highest ranked similar queries to the first log, and returns a report containing the similar queries. The processor further receives and executes a second query selected from the report.

Abstract: A memory stores a first log and a collection of queries. A processor accesses a second log that includes a first, second, third and fourth historical query, determines that the first and second historical queries belong to a first similarity group and the third and fourth historical queries belong to a second similarity group, and ranks the queries within groups based on static and dynamic features. The processor further determines the third historical query is frequently submitted and copies the highest ranked similar queries into the collection. The processor receives a first database query, stores it in both logs, determines it is not similar to queries stored in memory, further determines that it belongs to the first similarity group, copies the highest ranked similar queries to the first log, and returns a report containing the similar queries. The processor further receives and executes a second query selected from the report.

Abstract: A memory stores a first log and a collection of queries. A processor accesses a second log that includes a first, second, third and fourth historical query, determines that the first and second historical queries belong to a first similarity group and the third and fourth historical queries belong to a second similarity group, and ranks the queries within groups based on static and dynamic features. The processor further determines the third historical query is frequently submitted and copies the highest ranked similar queries into the collection. The processor receives a first database query, stores it in both logs, determines it is not similar to queries stored in memory, further determines that it belongs to the first similarity group, copies the highest ranked similar queries to the first log, and returns a report containing the similar queries. The processor further receives and executes a second query selected from the report.